Hi, sahim Get a 7-day free trial to premium PDF tools. ALL TOOLS Start free trial Export a PDF Edit text & images Convert to PDF Fill & Sign Organize pages Request e-signatures Compress a PDF Combine files Protect a PDF 1 55°F Cloudy Include the Dunding variable in regression models, rita murupie regression model using Days as the Y or dependent variable with independent variables of Hours, Material, the Hrs*mat interaction and the building indicators. What is the R-Squared for this model? Interpret the R-Squared value in the context of the problem. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.6197 0.3841 0.3272 7.2329 ANOVA a Regression 6 2120.37 ง Residual 65 3400.50 MS 353.40 52.32 6.76 Significance F 1.35522E-05 Total 71 5520.88 Coeficients Standard Erro A Intercept 2.784 1.2800 Stat 2.1749 Hours 3.447 0.6743 5.1126 Material 0.028 0.0070 3.9941 P-value 0.033278892 3.01597E-06 0.000168027 Athletic -1.695 5.1777 Academic -0.007 2.0862 -0.3274 0.744406626 -0.0031 0.997496893 Administrative -2.872 Hrs "mat -0.023 2.5728 0.0055 -1.1164 -4.1746 0.268348176 9.05865E-05 Q Search The R-Squared is 0.3841. This suggests that 32.14% of the variance in time to complete a work order (Days) is explained by the relationship with Hours, Material, Building type and the hours*material interaction. Page: 4 of 10 4 QMM2410 W24 Chp 16 HW problems Solution f) Using the model from part e), make a point prediction for the number of days to complete a work order for an administrative building that requires 3 hours, and $20 of material. Days 2.784+ 3.447(3) + 0.028(20) 1.695(0) 0.007(0) - 2.872(1) -0.023(3*20) = 9.43 g) Using the model from part e), provide a 90% confidence interval on the average number of days to complete a work order for an administrative building that requires 3 hours, and $20 of material. 9.43±1.669 (7.2329) √1/72 => [8.01, 10.85] H W 10 << > ENG 11:04 PM 4/12/2024 PRE
Hi, sahim Get a 7-day free trial to premium PDF tools. ALL TOOLS Start free trial Export a PDF Edit text & images Convert to PDF Fill & Sign Organize pages Request e-signatures Compress a PDF Combine files Protect a PDF 1 55°F Cloudy Include the Dunding variable in regression models, rita murupie regression model using Days as the Y or dependent variable with independent variables of Hours, Material, the Hrs*mat interaction and the building indicators. What is the R-Squared for this model? Interpret the R-Squared value in the context of the problem. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.6197 0.3841 0.3272 7.2329 ANOVA a Regression 6 2120.37 ง Residual 65 3400.50 MS 353.40 52.32 6.76 Significance F 1.35522E-05 Total 71 5520.88 Coeficients Standard Erro A Intercept 2.784 1.2800 Stat 2.1749 Hours 3.447 0.6743 5.1126 Material 0.028 0.0070 3.9941 P-value 0.033278892 3.01597E-06 0.000168027 Athletic -1.695 5.1777 Academic -0.007 2.0862 -0.3274 0.744406626 -0.0031 0.997496893 Administrative -2.872 Hrs "mat -0.023 2.5728 0.0055 -1.1164 -4.1746 0.268348176 9.05865E-05 Q Search The R-Squared is 0.3841. This suggests that 32.14% of the variance in time to complete a work order (Days) is explained by the relationship with Hours, Material, Building type and the hours*material interaction. Page: 4 of 10 4 QMM2410 W24 Chp 16 HW problems Solution f) Using the model from part e), make a point prediction for the number of days to complete a work order for an administrative building that requires 3 hours, and $20 of material. Days 2.784+ 3.447(3) + 0.028(20) 1.695(0) 0.007(0) - 2.872(1) -0.023(3*20) = 9.43 g) Using the model from part e), provide a 90% confidence interval on the average number of days to complete a work order for an administrative building that requires 3 hours, and $20 of material. 9.43±1.669 (7.2329) √1/72 => [8.01, 10.85] H W 10 << > ENG 11:04 PM 4/12/2024 PRE
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section: Chapter Questions
Problem 4SGR
Question
can anyone please explain part (g) ?what are these number and where did they come from? please refer to the regression anaylsis above since the answer for my question is related it to it
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